Our World In Data Vaccine Plots
rm(list=ls())
source("../DATA/movavg.R")
db <- db <- dbConnect(RSQLite::SQLite(),dbname= "../COVID-19-DB/OURWORLD.sqlite3")
SWE <- dbGetQuery(db,"select * from OWID")
SWE$date <- as.Date(SWE$date)
SWE <- SWE[order(SWE$date),]
df <- SWE %>%select(date,location,iso_code,new_vaccinations,
new_vaccinations_smoothed,total_vaccinations,population)
# df$VMA <- ma(df$new_vaccinations,6,centre=TRUE)
dbDisconnect(db)
World <- df %>% filter(location=="World" & date >="2020-12-10")
Plot of New Vaccinations for the World by Day
ggplot(World) + geom_line(aes(x=date,y=total_vaccinations)) +
scale_y_continuous(labels=comma) +
labs(title="Worldwide Total Vaccinations")
## Warning: Removed 5 row(s) containing missing values (geom_path).

Total New Vaccinations by Country
vaccine_date <- df %>% filter(location !="World") %>% na.omit() %>%
group_by(date) %>% summarise(Total = sum(new_vaccinations))
ggplot(vaccine_date) + geom_line(aes(x=date,y=Total)) + geom_smooth(aes(x=date,y=Total)) +
labs(title="Worldwide New Vaccinations by Date") +
scale_y_continuous(labels=comma)

Total Vaccinations by County (Top 5)
vaccine_country <- df %>% filter(location !="World") %>% na.omit() %>%
group_by(location) %>% summarise(Total = sum(new_vaccinations)) %>%
arrange %>% top_n(5,Total)
ggplot(vaccine_country) + geom_col(aes(x=location,y=Total)) +
scale_y_continuous(labels=comma) +
labs(title="Top Five Countries by New Vaccinations")

Plot of New and Total Vaccination for Israel
Israel <- df %>% filter(location =="Israel") %>% na.omit()
p1 <- ggplot(Israel) + geom_line(aes(x=date,y=new_vaccinations)) +
scale_y_continuous(labels=comma) +
labs(title="Israel New Vaccinations By Date") +
geom_smooth(aes(x=date,y=new_vaccinations))
p2 <- ggplot(Israel) + geom_line(aes(x=date,y=total_vaccinations)) +
scale_y_continuous(labels=comma) +
labs(title="Israel Total Vaccinations By Date")
Israel$Rate <- Israel$total_vaccinations/Israel$population
p3 <- ggplot(Israel) + geom_line(aes(x=date,y=Rate)) +
scale_y_continuous(labels=percent) +
labs(title="Israel vaccination Rate By Day")
ggplotly(p1)
ggplotly(p2)
ggplotly(p3)
US Vaccnations By Date
US <- df %>% filter(location =="United States") %>% na.omit()
summary(US$new_vaccinations)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 57909 651669 878059 886713 1125552 1561585
p5 <- ggplot(US) + geom_line(aes(x=date,y=new_vaccinations)) +
scale_y_continuous(labels=comma) +
labs(title="US New Vaccinations By Date") +
geom_smooth(aes(x=date,y=new_vaccinations))
p6 <- ggplot(US) + geom_line(aes(x=date,y=total_vaccinations)) +
scale_y_continuous(labels=comma) +
labs(title="US Total Vaccinations By Date")
US$Rate <- US$total_vaccinations/US$population
p7 <- ggplot(US) + geom_line(aes(x=date,y=Rate)) +
scale_y_continuous(labels=percent) +
labs(title="US vaccination Rate By Day")
ggplotly(p5)
ggplotly(p6)
ggplotly(p7)
Spain Vaccnations By Date
US <- df %>% filter(location =="Spain") %>% na.omit()
p8 <- ggplot(US) + geom_line(aes(x=date,y=new_vaccinations)) +
scale_y_continuous(labels=comma) +
labs(title="Spai nNew Vaccinations By Date") +
geom_smooth(aes(x=date,y=new_vaccinations))
p9 <- ggplot(US) + geom_line(aes(x=date,y=total_vaccinations)) +
scale_y_continuous(labels=comma) +
labs(title="Spain Total Vaccinations By Date")
ggplotly(p8)
ggplotly(p9)
US$Rate <- US$total_vaccinations/US$population
p10 <- ggplot(US) + geom_line(aes(x=date,y=Rate)) +
scale_y_continuous(labels=percent) +
labs(title="Spain vaccination Rate By Day")
ggplotly(p10)
United Kingdom Vaccnations By Date
US <- df %>% filter(location =="United Kingdom") %>% na.omit()
p8 <- ggplot(US) + geom_line(aes(x=date,y=new_vaccinations)) +
scale_y_continuous(labels=comma) +
labs(title="United Kingdom New Vaccinations By Date") +
geom_smooth(aes(x=date,y=new_vaccinations))
p9 <- ggplot(US) + geom_line(aes(x=date,y=total_vaccinations)) +
scale_y_continuous(labels=comma) +
labs(title="United Kingdom Total Vaccinations By Date")
ggplotly(p8)
ggplotly(p9)
US$Rate <- US$total_vaccinations/US$population
p10 <- ggplot(US) + geom_line(aes(x=date,y=Rate)) +
scale_y_continuous(labels=percent) +
labs(title="Kingdom vaccination Rate By Day")
ggplotly(p10)
India Vaccnations By Date
US <- df %>% filter(location =="India") %>% na.omit()
p8 <- ggplot(US) + geom_line(aes(x=date,y=new_vaccinations)) +
scale_y_continuous(labels=comma) +
labs(title="India New Vaccinations By Date") +
geom_smooth(aes(x=date,y=new_vaccinations))
p9 <- ggplot(US) + geom_line(aes(x=date,y=total_vaccinations)) +
scale_y_continuous(labels=comma) +
labs(title="India Total Vaccinations By Date")
ggplotly(p8)
ggplotly(p9)
US$Rate <- US$total_vaccinations/US$population
p10 <- ggplot(US) + geom_line(aes(x=date,y=Rate)) +
scale_y_continuous(labels=percent) +
labs(title="India vaccination Rate By Day")
ggplotly(p10)
Germany Vaccnations By Date
US <- df %>% filter(location =="Germany") %>% na.omit()
p8 <- ggplot(US) + geom_line(aes(x=date,y=new_vaccinations)) +
scale_y_continuous(labels=comma) +
labs(title="Germany New Vaccinations By Date") +
geom_smooth(aes(x=date,y=new_vaccinations))
p9 <- ggplot(US) + geom_line(aes(x=date,y=total_vaccinations)) +
scale_y_continuous(labels=comma) +
labs(title="Germany Total Vaccinations By Date")
ggplotly(p8)
ggplotly(p9)
US$Rate <- US$total_vaccinations/US$population
p10 <- ggplot(US) + geom_line(aes(x=date,y=Rate)) +
scale_y_continuous(labels=percent) +
labs(title="Germany vaccination Rate By Day")
ggplotly(p10)